Resistance to traditional antifungal agents is a considerable health problem nowadays, aggravated by infectious processes related to biofilm formation, usually on implantable devices. Therefore, it is necessary to identify new antimicrobial molecules, such as natural products, to develop new therapeutic strategies to prevent and eradicate these infections. One promising product is propolis, a natural resin produced by honeybees with substances from various botanical sources, beeswax and salivary enzymes. The aim of this work was to study the effect of a new Spanish ethanolic extract of propolis (SEEP) on growth, cell surface hydrophobicity, adherence and biofilm formation of Candida glabrata, a yeast capable of achieving high levels of resistance to available anti-fungal agents.
The antifungal activity of SEEP was evaluated in the planktonic cells of 12 clinical isolates of C. glabrata. The minimum inhibitory concentration (MIC) of propolis was determined by quantifying visible growth inhibition by serialsm.
The novel Spanish ethanolic extract of propolis shows antifungal activity against C. glabrata, and decreases biofilm formation. These results suggest its possible use in the control of fungal infections associated with biofilms.
The novel Spanish ethanolic extract of propolis shows antifungal activity against C. glabrata, and decreases biofilm formation. These results suggest its possible use in the control of fungal infections associated with biofilms.Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. https://www.selleckchem.com/products/azd3229.html Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.Systemic corticosteroid administration for severe acute exacerbations of COPD (AECOPD) reduces the duration of hospital stays. Corticosteroid-sparing regimens have showed non-inferiority to higher accumulated dose regimens regarding re-exacerbation risk in patients with AECOPD. However, it remains unclear whether 14-day or 2-5-day regimens would result in shorter admission durations and changes in mortality risk. We explored this by analysing the number of days alive and out of hospital based on two randomised controlled trials with different corticosteroid regimens.
We pooled individual patient data from the two available multicentre randomised trials on corticosteroid-sparing regimens for AECOPD the REDUCE (n?=?314) and CORTICO-COP (n?=?318) trials. In the 14-day regimen group, patients were older, fewer patients received pre-treatment with antibiotics and more patients received pre-treatment with systemic corticosteroids. Patients randomly allocated to the 14-day and 2-5-day regimens were compared, witeath or admission to ICU in COPD patients. Our results favour 2-5day regimens for treating COPD exacerbations. However, prospective studies are needed to validate these findings.
14-day corticosteroid regimens were associated with longer hospital stays and fewer days alive and out of hospital within 14 days, with no apparent 6-month benefit regarding death or admission to ICU in COPD patients. Our results favour 2-5 day regimens for treating COPD exacerbations. However, prospective studies are needed to validate these findings.The demand for home healthcare devices arises; however, many home healthcare devices on the market are not designed to reflect the needs and features of the end-users. This study explored the user knowledge factors that hindered the design of new home healthcare devices and the interrelationships between the factors.
The abovementioned factors were identified from analysing the project documents of thirty-eight carefully selected home healthcare devices produced by five manufacturers; followed by interviewing the thirty stakeholders playing key roles in developing the devices.
The design of the home healthcare devices was influenced by (1) the user insights utilised in formulating project strategies; (2) the sources of user information; (3) the execution of user research; and (4) the formulation of the manufacturers' principal innovation processes.
The users' characteristics and needs were not sufficiently reflected in developing new home healthcare devices. One root cause was that the end-users were not perceived by the manufacturers as a key success factor in most cases, given that most of the devices were initiated following the public sector's requests. Actual or potential applications of this study include the facilitation of the appropriate application of human factors methods in developing new home healthcare devices and the improvement of the user performance of the end-devices.
The users' characteristics and needs were not sufficiently reflected in developing new home healthcare devices. One root cause was that the end-users were not perceived by the manufacturers as a key success factor in most cases, given that most of the devices were initiated following the public sector's requests. Actual or potential applications of this study include the facilitation of the appropriate application of human factors methods in developing new home healthcare devices and the improvement of the user performance of the end-devices.